Color space values corresponding to classification identifiers

ABSTRACT

In an example, an image processing system may include an identification engine, a map engine, and a control engine. In that example, the identification engine may identify a color mapping resource in response to a classification identifier corresponding to image data, the map engine may map the image data with the color mapping resource having color space clause corresponding to the classification identifier, and the control engine may generate control data for operating a print apparatus to print based on the color mapping resource.

BACKGROUND

Images are processed for use with computing machines, such as a printapparatus. A print apparatus, for example, may use control data based onprocessed image data to reproduce a physical representation of an imageby operating a print fluid ejection system according to the controldata. Image processing may include color calibration. An image may beprocessed in a print apparatus pipeline or processed offline in separatecompute device, such as a print server.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1-4 are block diagrams depicting example image processing systems.

FIG. 5 depicts example engines and example operations of an exampleimage processing system.

FIGS. 6-8 are flow diagrams depicting example methods of color mappingfor use with a printing system.

DETAILED DESCRIPTION

In the following description and figures, some example implementationsof print apparatus, image processing systems, and/or methods of colormapping are described. In examples described herein, a “print apparatus”may be a device to print content on a physical medium (e.g., paper or alayer of powder-based build material, etc.) with a print fluid (e.g.,ink or toner). For example, the print apparatus may be a wide-formatprint apparatus that prints latex-based print fluid on a print medium,such as a print medium that is size A2 or larger. In the case ofprinting on a layer of powder-based build material, the print apparatusmay utilize the deposition of print fluids in a layer-wise additivemanufacturing process. A print apparatus may utilize suitable printconsumables, such as ink, toner, fluids or powders, or other rawmaterials for printing. In some examples, a print apparatus may be athree-dimensional (3D) print apparatus. An example of print fluid is awater-based latex ink ejectable from a print head, such as apiezoelectric print head or a thermal inkjet print head. Other examplesof print fluid may include dye-based color inks, pigment-based inks,solvents, gloss enhancers, fixer agents, and the like.

Certain examples described herein relate to color calibration of a printsystem. For example, color calibration may be used to adjust the colorresponse of the print system to more accurately correspond to a desiredcolor to be printed. Color calibration may be used to calibrate a colormapping process by which a first representation of a given color ismapped to a second representation of the same color. The concept of“color” can be represented in a large variety of ways, such as inrelation to a power or intensity spectrum of electromagnetic radiationacross a range of visible wavelengths or a color model is used torepresent a color at a lower dimensionality. A “color” may be said to bea category that is used to denote similar visual perceptions where twocolors are said to be similar if they produce a similar effect on agroup of one or more people. These categories can then be modelled usinga lower number of variables. In an example printing pipeline, individualinks may be calibrated separately so that printed colors are similar toor match desired colors.

A color model may define a color space (i.e., a multi-dimensional spacewith dimensions of the space representing variables within the colormodel and a point in the multi-dimensional space representing a colorvalue. For example, in a red, green, blue (RGB) color space, an additivecolor model defines three variables representing different quantities ofred, green and blue light. Another color space may include a cyan,magenta, yellow and black (CMYK) color space, in which four variablesare used in a subtractive color model to represent different quantitiesof colorant or ink (e.g., for a print system and an image with a rangeof different colors can be printed by overprinting images for each ofthe colorants or inks). Yet other examples include: the InternationalCommission on Illumination (CIE) 1931 XYZ color space, in which threevariables (‘X’, ‘Y’ and ‘Z’ or tristimulus values) are used to model acolor; the CIE 1976 (L*, a*, b*—CIELAB or ‘LAB’) color space, in whichthree variables represent lightness (‘L’) and opposing color dimensions(‘a’ and ‘b’); and the Yu′v′ color space, in which three variablesrepresent the luminance (‘Y’) and two chrominance dimensions (u′ andv′).

Other spaces include area coverage spaces, such as the NeugebauerPrimary area coverage (NPAC) space. NPAC space may be used as a printcontrol space that controls a color output of an imaging device. An NPACvector in the NPAC space represents a statistical distribution of one ormore Neugebauer Primary (NP) vectors over an area of a halftone. An NPmay include one of N^(k) combinations of k inks within the print system,such as in the context of multi-level printers where print heads areable to deposit N drop levels (e.g., via multiple print passes orprint-bars). For example, in a simple binary (bi-level, i.e. two dropstate: “drop” or “no drop”) printer, an NP is one of 2^(k) combinationsof k inks within the print system. In an example of a print apparatusthat uses CMY inks, there can be eight NPs: C, M, Y, C+M, C+Y, M+Y,C+M+Y, and W (white or blank indicating an absence of ink). An NP maycomprise an overprint of available inks, such as a drop of magenta on adrop of cyan (for a bi-level printer) in a common addressable print area(e.g. a printable “pixel”).

An NPAC space provides a large number of metamers. Metamerism is theexistence of a multitude of combinations of reflectance and emissionproperties that result in the same perceived color for a fixedilluminant and observer.

Each NPAC vector may define the probability distribution for one or morecolorant or ink combinations for each pixel in a halftone (e.g., alikelihood that a particular colorant or ink combination is to be placedat each pixel location in the halftone). In this manner, a given NPACvector defines a set of halftone parameters that can be used in thehalftoning process to map a color to a NP vector (e.g., map a color tomultiple NP vectors) to be statistically distributed over the pluralityof pixels for a halftone. Moreover, the statistical distribution of NPsto pixels in the halftone serves to control the colorimetry and otherprint characteristics of the halftone. Spatial distribution of the NPsaccording to the probability distribution specified in the NPAC vectormay be performed using any suitable halftoning methods, such asmatrix-selector-based Parallel Random Area Weighted Area CoverageSelection (PARAWACS) techniques and techniques based on error diffusion.An example of a print system that uses area coverage representations orhalftone generation is a Halftone Area Neugebauer Separation (HANS)pipeline.

In general, color separations may use many different NPs at differentprobabilities (i.e., coverages) to represent a given color. Largeregions or smoothly changing color transitions (where such NP varietymay help grain or redundancy) may benefit from the use of many differentNPs. However, other detail features, such as for edges and lines,complex NPs (e.g., the use of many different NPs) may result indecreased integrity to the source image.

Various examples described below relate to color calibration forprinting based on NPACs, for example using a HANS pipeline. The colorcalibration may involve adjustment to the color space values based on aclassification of the image data, e.g., an area of fine detail (such aslines or text) or uniform color area.

The terms “include,” “have,” and variations thereof, as used herein,mean the same as the term “comprise” or appropriate variation thereof.Furthermore, the term “based on,” as used herein, means “based at leastin part on.” Thus, a feature that is described as based on some stimulusmay be based only on the stimulus or a combination of stimuli includingthe stimulus.

FIGS. 1-4 are block diagrams depicting example image processing systems100, 200, 300, and 400. Referring to FIG. 1, the example imageprocessing system 100 of FIG. 1 generally includes an identificationengine 102, a map engine 104, and a control engine 106. In general, mapengine 104 performs a color mapping operation using a resourceidentified by the identification engine 102 using classification ofimage data and the results of the color mapping operation are used bythe control engine 106 to produce control data to operate a printapparatus based on the image classification.

The identification engine 102 represents any circuitry or combination ofcircuitry and executable instructions to an identification engine toidentify a color mapping resource in response to a classificationidentifier corresponding to image data. For example, the identificationengine 102 may be a combination of circuitry and executableinstructions, that when executed, identify a set of resources to usebased on classification identifiers corresponding to regions of imagedata (e.g., a resource for each region or each classification applied tothe regions of image data). With availability of regionalclassifications of the image data, the identification engine 102 is ableto determine a resource to use in the color mapping operations, such asa color separation process. For example, a subset of a canonical set ofresources may be used, where the subset of resources are modified (i.e.,different with respect to the canonical set) to generate mapping resultsthat preserve integrity for regions with that regional classification,such as resources representing simplified coverages for edges and lines.Indeed, the identification engine 102 may identify a coverage attributethreshold that corresponds to the classification identifiers associatedwith the regions, and identify a coverage attribute threshold that isdifferent from a default coverage size threshold for any given regionthat is classified, for example. With the appropriate resourcesselected, the color mapping operation performed by the image processingsystem 100 may perform the color mapping operations with betterintegrity to the source data, for example.

As used herein, a “resource” may include any set of reference data orfunctions useable with the color mapping operations. Example resourcesinclude a look-up table (LUT) of color values based on coverage size, acolor separation function, a halftone matrix, a halftone processingfunction, the like, or a combination thereof. A “coverage attributethreshold,” as used herein, represents a threshold amount of acharacteristic of coverage of a color in a color space of an area.Example coverage attribute thresholds include a coverage size thresholdand a coverage complexity threshold. As used herein, a “coverage sizethreshold” refers to the amount (e.g., a proportion) of coverage of acolor in a pixel area. Coverages may be expressed as a probability orproportion over a nominal area, such a percentage of the area having theparticular color combination. For example, a green area may be producedby a printing 50% of the pixels of an area with cyan and 50% of thepixels of the area with yellow, where an area with 50% coverage of cyanand 50% coverage of yellow produces a green tone. In that example, acoverage size threshold of 30% would not limit (e.g., eliminate) eitherthe cyan or yellow coverages because the coverages of the colors areabove the threshold amount. In this manner, a coverage size and/or acoverage size threshold may be represented in any appropriate formcapable of representing an amount of coverage of an area, such as apercentage, a decimal, or any other number or character stringassociable with coverage amount. As used herein, a “coverage complexitythreshold” refers to a number of colors in a coverage area, such as anumber of NPs per NPAC. For example, a coverage complexity may bereduced to a threshold number by removing color combinations from theNPAC until a desired number of color combinations is achieved. In thismanner, a coverage complexity threshold may be represented in anyappropriate form representing a number of different color combinationsin an area. Example colors include base colors of a colors space (suchas cyan, magenta, yellow, and black in a CMYK color space) as well asvariations of those colors (such as CM and MY in the example CMYK colorspace). White coverage and/or blank coverages are also possibleselections for representing a coverage area. For example, a CMYK NPACmay include the following percentages of colors: C at 3.5%, M at 7%, CMat 12%, Y at 43%, MY at 23.5%, and blank at 10%. The thresholdsdiscussed herein can be used for relative modifications to color spaces.For example, a coverage size threshold of 4% would remove the C from theprevious example and a coverage complexity threshold of 4 would removethe C and M, leaving the area coverage represented by the top four colorcombinations: Y, MY, CM, and blank. In such examples, the remainingcolor coverages may be rescaled to compensate for any modifications.

The map engine 104 represents any circuitry or combination of circuitryand executable instructions to map the image data with the color mappingresource having color space values corresponding to the classificationidentifier. For example, the map engine 104 may be a combination ofcircuitry and executable instructions that, when executed, perform colorseparation operations using a LUT of color spaces corresponding to theclassification identifier, as selected by the identification engine 102.The map engine 104 may use a resource directly with the color mappingoperations or may use the resource to produce a modified version of theresource to use with the color mapping operations. For example, the mapengine 104 may select a LUT from a number of predetermined LUTs, such asselecting a LUT associated with coverage attribute threshold and usingan identified coverage attribute threshold that is different from adefault coverage attribute threshold. For another example, the mapengine 104 may retrieve a general LUT (e.g., canonical LUT) and based onthe retrieved resource, determine a modification for the general LUT andcompute a modified version of the general LUT, such as retrieving datathat the table should have a particular coverage attribute threshold,and modifying a general LUT to remove any color space values beyond thecoverage attribute threshold identified by the identification engine102. Modified versions of resources may include adding or removingNPACs, such as by increasing or decreasing the coverages of NPs incomparison to a canonical LUT.

The map engine 104 may utilize the feature classifications and NPACs ofthe LUTs corresponding to the feature classifications to individualizethe color mapping process for each image region based on the featureclassification associated with it. In an example, a minimum number ofNPs per node may be determined by setting every NP to have as large anarea coverage as possible via removing area coverages that correspond tocoverages below a threshold and distributing the coverages to NPs thatremain after the cut off. In that manner, the LUT ends up being lessgranular, for example, and may be used to preserve detail features. Inanother example, a print apparatus with multiple print bars may elect toprint regions classified as details with a single print bar with amodified color separation based on an area coverage threshold so that ifan RGB node in a pipeline was [0 0 0: w:0.8, KKK:0.2]—meaning 3 drops ofK are printed at 40% coverage for the black node, in the dynamicallymasked case (where instead of CMYK we have 12 channels, e.g.CMYKEFGHOPQR, whereby CMYK is the first bar, EFGH is CMYK for the secondbar and OPQR is CMYK for the third bar) this would translate to [0 0 0:w:0.8, KHR:0.2] and the mask would be modified to [0 0 0:w:0.4, K:0.6](of H or R at 60%) during the color mapping process when printing with asingle print bar (and the multiple print bar alternative would be [0 00: w:0.4, K:0.2, H:0.2, R:0.2]). In that example, the drop placementerror due to multi-bar alignment may be reduced in the detail regions.The opposite may also be true; for example, a non-detail region may beenhanced by adding noise into the color mapping process by utilizingmultiple print bars derived from a single print bar mapping alternative.

The control engine 106 represents any circuitry or combination ofcircuitry and executable instructions to generate control data foroperating a print apparatus to print based on the color mappingresource. For example, the control engine 106 may be a combination ofcircuitry and executable instructions, that when executed, generate anoutput image halftone as a result of the color mapping operations for aprint apparatus able. Other control data particular to theimplementation of the processing pipeline of the print apparatus may beused. For another example, the control engine 106 may combine the colormapping operations performed on the regions of the image data that havedifferent classifications. For yet another example, the control engine106 may generate control data by combining a first set of halftone imagedata processed using a first LUT with image data corresponding toidentified first coverage attribute threshold and a second set ofhalftone image data processed using a second LUT corresponding to asecond coverage attribute threshold that is different from the firstcoverage attribute threshold. The result produced by the control data ofthe control engine 106 may be a halftone that is homogenous when usingresources that originated from similar resources, such as a canonicalLUT for general or default processing and a modified LUT from thecanonical LUT using sufficiently high area coverages (or sufficientlylow area coverages depending on the desired mapping effect) thatpreserves the regions associated with feature classifications byprocessing them with the modified resources.

Referring to FIG. 2, the print system 200 may be implemented on a printapparatus 210 that includes a print fluid ejection system 212 to depositcolorants according to the control data onto a print medium. The printsystem 200 includes an identification engine 202, a map engine 204, anda control engine 206 that are the same as the identification engine 102,the map engine 104, and the control engine 106 of FIG. 1 and may includeparticular implementations specific to utilize the print fluid ejectionsystem 212 of the print apparatus 210 and the classifier engine 210.

The classifier engine 208 represents any circuitry or combination ofcircuitry and executable instructions to determine the classificationidentifier based on a characteristic of the image data. For example, theclassification identifiers may be included in raster image processing(RIP) data as a result of processing the image source by a RIP processorof the classifier engine 208. The classifier engine 208 may utilizeimage processing techniques for classification of image regions, such asedge detection or gradient filter computations, which may result in afeature map or equivalent by distinguishing brightness as a sharpergradient, for example.

FIGS. 3 and 4 depict the example systems 300 and 400 may comprise amemory resource operatively coupled to a processor resource. Referringto FIG. 3, the memory resource 320 may contain a set of instructionsthat are executable by the processor resource 322. The set ofinstructions are operable to cause the processor resource 322 to performoperations of the system 300 when the set of instructions are executedby the processor resource 322. The set of instructions stored on thememory resource 320 may be represented as an identification module 302,a map module 304, and a control module 306. The identification module302, the map module 304, and the control module 306 represent programinstructions that when executed function as the identification engine102, the map engine 104, and the control engine 106 of FIG. 1,respectively. The processor resource 322 may carry out a set ofinstructions to execute the modules 302, 304, 306 and/or any otherappropriate operations among and/or associated with the modules of thesystem 300. For example, the processor resource 322 may carry out a setof instructions to perform a color mapping process using color spacevalues derived form a modified color space based on a classificationidentifier corresponding to input image data where the modified colorspace includes area coverages that are increased (e.g., on average) whenthe classification identifier corresponds to a first feature (e.g., adetail region) or decreased (e.g., on average) when the classificationidentifier corresponds to a second feature (e.g., a non-detail region).For another example, the processor resource 322 may carry out a set ofinstructions to identify a threshold coverage attribute associated witha classification identifier and modify a LUT of color space values toinclude modified color space values by either removing coverages inorder of coverage size until a threshold number of NPs is achieved(e.g., remove NPs with smallest area coverages until the desired maximumnumber of NPs is obtained) and resealing any remaining coverage areas oradding noise to the color space values above the coverage attributethreshold and resealing any remaining coverage areas. For yet anotherexample, the processor resource 322 may carry out a set of instructionsto remove NPs from an NPAC retrieved from a LUT by omission of NPs witha color area coverage less than the coverage size threshold from the LUTentry or elimination of NPs with a relatively smallest area coverageuntil a coverage complexity threshold is achieved, maintain a coveragesize of a blank NP with respect to unmodified color space values, andrescale non-blank NPs to increase coverage in response to the removal ofNPs. For yet another example, the processor resource 322 may carry out aset of instructions to select a halftone resource based on theclassification identifier, produce an output image halftone using theselected halftone resource, and generate control data for a printapparatus from the output image halftone.

Although these particular modules and various other modules areillustrated and discussed in relation to FIG. 3 and other exampleimplementations, other combinations or sub-combinations of modules maybe included within other implementations. Said differently, although themodules illustrated in FIG. 3 and discussed in other exampleimplementations perform specific functionalities in the examplesdiscussed herein, these and other functionalities may be accomplished,implemented, or realized at different modules or at combinations ofmodules. For example, two or more modules illustrated and/or discussedas separate may be combined into a module that performs thefunctionalities discussed in relation to the two modules. As anotherexample, functionalities performed at one module as discussed inrelation to these examples may be performed at a different module ordifferent modules. FIG. 4 depicts yet another example of howfunctionality may be organized into modules as an image processingsystem 400 useable as part of a print apparatus 410, including aclassifier module 408 that represents program instructions that whenexecuted function as the classifier engine 208 of FIG. 2.

A processor resource is any appropriate circuitry capable of processing(e.g., computing) instructions, such as one or multiple processingelements capable of retrieving instructions from a memory resource andexecuting those instructions. For example, the processor resource 322may be a central processing unit (CPU) that enables color mapping basedon classification identifier by fetching, decoding, and executingmodules 302, 304, and 306. For another example, the processor resource422 may be a central processing unit (CPU) that enables color mappingbased on classification identifier by fetching, decoding, and executingmodules 402, 404, 406, and 408. Example processor resources include atleast one CPU, a semiconductor-based microprocessor, a programmablelogic device (PLD), and the like, Example PLDs include an applicationspecific integrated circuit (ASIC), a field-programmable gate array(FPGA), a programmable array logic (PAL), a complex programmable logicdevice (CPLD), and an erasable programmable logic device (EPLD). Aprocessor resource may include multiple processing elements that areintegrated in a single device or distributed across devices. A processorresource may process the instructions serially, concurrently, or inpartial concurrence.

A memory resource represents a medium to store data utilized and/orproduced by the system 300 or 400. The medium is any non-transitorymedium or combination of non-transitory media able to electronicallystore data, such as modules of the systems 300, 400 and/or data used bythe systems 300, 400. For example, the medium may be a storage medium,which is distinct from a transitory transmission medium, such as asignal. The medium may be machine-readable, such as computer-readable.The medium may be an electronic, magnetic, optical, or other physicalstorage device that is capable of containing (i.e., storing) executableinstructions. The memory resources 320 and 420 may be said to storeprogram instructions that when executed by the processor resources 322or 422, respectively, cause the processor resources 322 or 422 toimplement functionality of the system 300 of FIG. 3 and system 400 ofFIG. 4, respectively. A memory resource may be integrated in the samedevice as a processor resource or it may be separate but accessible tothat device and the processor resource. The memory resource may bedistributed across devices.

In the discussion herein, the engines 102, 104, 106, 202, 204, 206, and208 of FIGS. 1-2 and the modules 302, 304, 306, 402, 404, 406, and 408of FIGS. 3-4 have been described as circuitry or a combination ofcircuitry and executable instructions. Such components may beimplemented in a number of fashions. Looking at FIG. 3, the executableinstructions may be processor-executable instructions, such as programinstructions, stored on a memory resource, such as memory resource 320,which is a tangible, non-transitory computer-readable storage medium,and the circuitry may be electronic circuitry, such as processorresource 322, for executing those instructions. The instructionsresiding on the memory resource may comprise any set of instructions tobe executed directly (such as machine code) or indirectly (such as ascript) by the processor resource.

In some examples, the system 400 may include executable instructionsintegrated on a memory resource coupled directly to a processor resourceon a circuit board of a device, such as a print apparatus 410. In someexamples, the system 300 may include executable instructions that may bepart of an installation package that when installed may be executed bythe processor resource 322 to perform operations of the system 300, suchas methods described with regards to FIGS. 5-8. In that example, thememory resource 320 may be a portable medium such as a compact disc, adigital video disc, a flash drive, or memory maintained by a computerdevice, such as a server, from which the installation package may bedownloaded and installed. In another example, the executableinstructions may be part of an application or applications alreadyinstalled. A memory resource may be a non-volatile memory resource suchas read only memory (ROM), a volatile memory resource such as randomaccess memory (RAM), a storage device, or a combination thereof. Exampleforms of a memory resource include static RAM (SRAM), dynamic RAM(DRAM), electrically erasable programmable ROM (EEPROM), flash memory,or the like. The memory resource may include integrated memory such as ahard drive (HD), a solid state drive (SSD), or an optical drive.

As mentioned above, the engines 102, 104, 106, 202, 204, 206, and 208may be integrated via circuitry or as installed instructions into amemory resource of the compute device, such as a print apparatus or aservice device (e.g., a web server). A service device representsgenerally any compute device to respond to a network request receivedfrom a user device (e.g., a print apparatus), whether virtual or real.For example, the service device may operate a combination of circuitryand executable instructions to provide a network packet in response to arequest for a page or functionality of an application. A user devicerepresents generally any compute devices to communicate a networkrequest and receive and/or process the corresponding responses. Thecompute devices may be located on separate networks 330 or part of thesame network 330. For example, the compute devices may generally becoupled by a link, where a link generally represents one or acombination of a cable, wireless connection, fiber optic connection, orremote connections via a telecommunications link, an infrared link, aradio frequency link, or any other connectors of systems that provideelectronic communication including communication with, at least in part,intranet, the Internet, or a combination of both (e.g., and may useintermediate proxies, routers, switches, load balancers, and the like).Any appropriate combination of the system 300 and compute devices may bea virtual instance of a resource of a virtual shared pool of resources.The engines and/or modules of the system 300 herein may reside and/orexecute “on the cloud” (e.g., reside and/or execute on a virtual sharedpool of resources).

Referring to FIGS. 1-4, the engines 102, 104, 106, 202, 204, 206, and208 of FIGS. 1-2 and/or the modules 302, 304, 306, 402, 404, 406, and408 of FIGS. 3-4 may be distributed across devices. The engine and/ormodules may complete or assist completion of operations performed indescribing another engine and/or module. For example, the map engine 104of FIG. 1 may request, complete, or perform the methods or operationsdescribed with the map engine 204 of FIG. 2 as well as theidentification engine 202, the control engine 206, and the classifierengine 208 of FIG. 2. Thus, although the various engines and modules areshown as separate engines in FIGS. 1-4, in other implementations, thefunctionality of multiple engines and/or modules may be implemented as asingle engine and/or module or divided in a variety of engines and/ormodules. In some examples, functionalities described herein in relationto any of FIGS. 1-4 may be provided in combination with functionalitiesdescribed herein in relation to any of FIGS. 5-8.

FIG. 5 depicts example engines and example operations of an exampleimage processing system 500. Referring to FIG. 5, the example engines ofFIG. 5 generally include a classifier engine 508, an identificationengine 502, a map engine 504, and a control engine 506 that are similarto the classifier engine 208, the identification engine 202, the mapengine 204, and the control engine 206 of FIG. 2, respectively. Theassociated descriptions have not been repeated in their entirety forbrevity. The example engines of FIG. 5 may be implemented on a computedevice, such as large format print apparatus.

As depicted in FIG. 5, the classifier engine 508 may receive the inputimage 530 and perform a classification analysis on the input image 530.The classifier engine 508 associates regions of the input image 530 withclassification identifiers. The classifier engine 508 may pass theregional classification associations to the identification engine 502.

The identification engine 502 uses the regional classificationinformation to identify a color mapping resource for a region of theinput image 530. In the example of FIG. 5, the identification engine 502identifies a first LUT 544 (e.g., to use with the first color separationprocess 532 of a first region of the input image) and a first halftonematrix 548 (e.g., to use in the first halftone process 536 of the resultof the first color separation process 532). The identification engine502 may identify resources for each region (e.g., a second LUT 546 and asecond halftone matrix 550) and/or a default set of resources may beused for other regions of the input image 530. The halftone processand/or color separation process may also be considered a resource thatmay be selected by the identification engine 502 for use with the mapengine 504.

The result of the first halftone process 536 (and any other halftoneprocesses, such as second halftone process 538) may be received by thecontrol engine 506 to produce control data useable to produce an outputimage 542, such control data useable to instruct a printing apparatus.The control engine 506 may include a combination engine 540 whichrepresents circuitry or a combination of circuitry and executableinstructions to combine the results of the halftone processes (e.g.,output halftone images of the first halftone process 536 and secondhalftone process 538) into data useable to generate image control data,such as an output halftone image of the input image as processed bycolor mapping operations.

Although FIG. 5 conceptually depicts two different pipelines, the imageprocessing system may be implemented as multiple pipelines orimplemented as a single pipeline with a larger set of NPs. For furtherexample, interpolation may be performed as with a single LUT, while inthe matrix halftoning process, the tiles and text flag attached to eachpixel may determine if the matrix value is compared against the first orthe second set of NPs. In that example, the logic block in an FPGA maybe, for example, more compact and smaller by transferring a large tableinstead of multiple small tables.

FIGS. 6-8 are flow diagrams depicting example methods of color mappingfor use with a print apparatus, such as a print apparatus with multipleprint bars. Referring to FIG. 6, example methods of color mapping maygenerally comprise determining to use a number of print bars, selectinga LUT of color space values based on a classification identifier,performing a color mapping process using the selected LUT, andgenerating control data for printing based on a result of the colormapping process. The operations discussion herein with reference toFIGS. 6-8 may be performable by the engines and systems of FIGS. 1-5, asappropriate.

At block 602, a number of print bars to use is determined based on aclassification identifier. For example, a number of print bars to usefor printing a first set of image data (e.g., representing a firstregion of the image) is determined based on a first classificationidentifier corresponding with the first set of image data. For example,a single print bar may be selected for use for printing details, such aslines, while a plurality of print bars may be used to print large colorfill areas. The operations of block 602 may be performed by anidentification engine, such as the identification engine 102 of FIG. 1.

At block 604, a LUT of colors space values is selected based on thefirst classification identifier. For example, a first LUT of color spacevalues is selected based on a first classification identifier and thefirst LUT has color space values with coverage sizes that are differentthan default color space values corresponding to unclassified imagedata. The operations of block 604 may be performed by an identificationengine, such as the identification engine 102 of FIG. 1.

At block 606, a color mapping process is performed using the LUTcorresponding to the classification identifier as identified at block604. For example, the color mapping process for a first set of imagedata is performed using a first LUT selected at block 604 correspondingto the first classification identifier. The operations of block 606 maybe performed by a map engine, such as the map engine 104 of FIG. 1.

At block 608, control data is generated for printing a halftone imagebased on a result of the color mapping process performed on the firstset of image data at block 606 using the LUT identified at block 604.For example, control data for printing a halftone image is generatedbased on a result of the color mapping process performed at block 606using the first LUT corresponding to the first classification identifieridentified at block 604. The operations of block 608 may be performed bya control, engine, such as the control engine 106 of FIG. 1.

FIGS. 7 and 8 include blocks similar to blocks of FIG. 6 and provideadditional blocks and details. In particular, FIG. 7 depicts additionalblocks and details generally regarding classifying the image data,retrieving NPACs, and generating a halftone image, and FIG. 8 depictsadditional blocks and details generally regarding identifying a numberclassification identifiers, selecting halftone processes with theclassification identifiers, and using the halftone processes to generatean output halftone image. The blocks of FIGS. 7 and 8 include blockssimilar to blocks of FIG. 6 and, for brevity, the descriptions of FIG. 6are not repeated in their entirety for the descriptions of FIGS. 7 and8.

At block 702, classification identifiers associated with an image areidentified. For example, a first classification identifier associatedwith a first set of image data (e.g., a first region of the image) and asecond classification identifier associated with a second set of imagedata (e.g., a second region of the image) are identified based oncharacteristics of the color data of those sets of data. Theclassification identifiers may be identified by a classifier engine,such as classifier engine 208 of FIG. 2.

At block 704, a number of print bars to use for each set of image datais identified based on the first classification identifier and thesecond classification identifier. At block 706, a first LUT and secondLUT of color space values are selected based on the first and secondclassification identifiers, respectively, at block 702. The LUTsselected at block 706 are used to retrieve a first NPAC from the firstLUT and a second NPAC from the second LUT at block 708. At block 710,the color mapping process is performed using the NPACs retrieved atblock 708 for the corresponding image data sets. A halftone image isgenerated at block 712 using a first halftone matrix corresponding tothe first classification identifier and a second halftone matriccorresponding to the second classification identifier. The first LUTincludes NPACs with a number of NPs per NPAC that is different from anumber of NPs per NPAC of the second LUT (and the NPs may have rescaledarea coverages corresponding to the difference in area coverage of theNPs in corresponding LUT entries). For example, the first LUT mayinclude NPACs with fewer NPs per NPAC than the number of NPs per NPAC ofthe second LUT to reduce the number of colors for use with details (andincrease the coverage size of the remaining NPs proportionally). Foranother example, the first LUT may include NPACs with more NPs per NPACthan the number of NPs per NPAC of the second LUT to add noise for areafill integrity of non-detail regions (and decrease the coverage size ofthe original NPs proportionally to accommodate the additional NPs).

Referring to FIG. 8, a number of classification identifiers areidentified as associated with an input image at block 802. For example,a classification identifier may be determined for each type of featureidentifiable in a feature map derived from a classifier engine, such asclassifier engine 208. At block 804, a number of print bars to use isidentified for each classification identifier. In this manner, a numberof print bars may be associated with each region of the image.

At block 806, a LUT of colors space values and a halftone process areselected for each classification identifier associated with the inputimage data. For example, a bundle of resources including an LUT, ahalftone matrix, and a halftone processing operation are selected basedon the classification identifier for the region. At block 808, a colormapping process is performed for each image data set corresponding tothe classification identifier using the selected resources (e.g., usingthe LUT corresponding to the classification identifier).

At block 810, an output halftone image is generated by performing theselected halftone processes on the corresponding image data sets usingthe LUT corresponding to the associated classification identifiers. Forexample, the result of a color separation process using the LUTs for animage region corresponding to a particular image classification isprocessed with a halftone process corresponding to the imageclassification using a halftone matrix corresponding to the imageclassification. Separate halftone process results may be combined or thehalftone process may utilize the multiple regions and halftone resourcesas inputs to produce an output halftone image. The halftone processingresults are used to generate an output halftone image and the outputhalftone image is used at block 812 to generate control data forprinting the input image by a print apparatus. By providing modifiedresources, custom features in an image may be, for example, processeddifferently than general regions of an image and the images may be, forexample, processed in a homogenous way with content-type color mappingaccommodations.

Although the flow diagrams of FIGS. 5-8 illustrate specific orders ofexecution, the order of execution may differ from that which isillustrated. For example, the order of execution of the blocks may bescrambled relative to the order shown. Also, the blocks shown insuccession may be executed concurrently or with partial concurrence. Allsuch variations are within the scope of the present description.

All of the features disclosed in this specification (including anyaccompanying claims, abstract and drawings), and/or all of the elementsof any method or process so disclosed, may be combined in anycombination, except combinations where at least some of such featuresand/or elements are mutually exclusive.

The present description has been shown and described with reference tothe foregoing examples. It is understood, however, that other forms,details, and examples may be made without departing from the spirit andscope of the following claims. The use of the words “first,” “second,”or related terms in the claims are not used to limit the claim elementsto an order or location, but are merely used to distinguish separateclaim elements.

What is claimed is:
 1. An image processing system comprising: a memorystoring instructions; and a processor, by executing the instructionsstored in the memory, to: identify a color mapping resource in responseto a classification identifier corresponding to image data; identify acoverage attribute threshold corresponding to the classificationidentifier that is different from a default coverage attributethreshold; select a look-up table (LUT) corresponding to the identifiedcoverage attribute threshold that is different from the default coverageattribute threshold; map the image data with the color mapping resourcehaving color space values corresponding to the classificationidentifier; and generate control data for operating a print apparatus toprint based on the color mapping resource.
 2. The system of claim 1,further comprising: a print fluid ejection system to deposit colorantsaccording to the control data, wherein the processor, by furtherexecuting the instructions stored in the memory, is to determine theclassification identifier based on a characteristic of the image data.3. The system of claim 1, wherein the classification identifiercorresponds to a region of the image data.
 4. The system of claim 3,wherein the region of the image data includes an edge or a line.
 5. Animage processing system comprising: a memory storing instructions; and aprocessor, by executing the instructions stored in the memory, to:identify a color mapping resource in response to a classificationidentifier corresponding to image data; retrieve a general look-up table(LUT); compute a modified version of the general LUT based on anidentified coverage attribute threshold; map the image data with thecolor mapping resource having color space values corresponding to theclassification identifier; and generate control data for operating aprint apparatus to print based on the color mapping resource.
 6. Thesystem of claim 5, further comprising: a print fluid ejection system todeposit colorants according to the control data, wherein the processor,by further executing the instructions stored in the memory, is todetermine the classification identifier based on a characteristic of theimage data.
 7. An image processing system comprising: a memory storinginstructions; and a processor, by executing the instructions stored inthe memory, to: identify a color mapping resource in response to aclassification identifier corresponding to image data; map the imagedata with the color mapping resource having color space valuescorresponding to the classification identifier; and generate controldata for operating a print apparatus to print based on the color mappingresource by combining a first set of halftone image data processed usinga first look-up table (LUT) with image data corresponding to a firstcoverage attribute threshold and a second set of halftone image dataprocessed using a second LUT corresponding to a second coverageattribute threshold that is different from the first coverage attributethreshold.
 8. The system of claim 7, further comprising: a print fluidejection system to deposit colorants according to the control data,wherein the processor, by further executing the instructions stored inthe memory, is to determine the classification identifier based on acharacteristic of the image data.
 9. A non-transitory computer-readablestorage medium comprising a set of instructions executable by aprocessor resource to: perform a color mapping process using color spacevalues derived from a modified color space based on a classificationidentifier corresponding to input image data, wherein the modified colorspace includes color area coverages that are: increased when theclassification identifier corresponds to a detail feature; or decreasedwhen the classification identifier corresponds to a non-detail feature.10. The medium of claim 9, wherein the color space values are NeugebauerPrimaries (NPs) and the color area coverages are Neugebauer Primary AreaCoverages (NPACs).
 11. The medium of claim 10, wherein the set ofinstructions is executable by the processor resource to: modify alook-up table (LUT) of color space values to include the modified colorspace values by: removing coverages in order of coverage size until athreshold number of NPs is achieved and rescaling any remaining coverageareas; or adding noise to the color space values above a coverageattribute threshold and rescaling any remaining coverage areas.
 12. Themedium of claim 10, wherein the set of instructions is executable by theprocessor resource to: remove NPs from an NPAC retrieved from a LUT by:omission of NPs with a color area coverage less than a coverage sizethreshold or elimination of NPs with a relatively smallest area coverageuntil a coverage complexity threshold is achieved; maintain a coveragesize of a blank NP with respect to unmodified color space values; andrescale coverages of non-blank NPs to increase coverage in response tothe removal of NPs.
 13. The medium of claim 10, wherein the set ofinstructions is executable by the processor resource to: select ahalftone resource based on the classification identifier; produce anoutput image halftone using the selected halftone resource; and generatecontrol data for a print apparatus from the output image halftone. 14.The medium of claim 9, wherein the classification identifier correspondsto a region of the image data.
 15. The medium of claim 14, wherein theregion of the image data includes an edge or a line.
 16. A method ofcolor mapping for use with a print apparatus having multiple print barscomprising: determining to use a number of print bars based on a firstclassification identifier corresponding with a first set of image data;selecting a first look-up table (LUT) of color space values based on thefirst classification identifier, the first LUT having color space valueswith coverage sizes that are different than default color space valuescorresponding to unclassified image data; performing a color mappingprocess for the first set of image data using the first LUTcorresponding to the first classification identifier; and generatingcontrol data for printing a halftone image based on a result of thecolor mapping process performed on the first set of image data using thefirst LUT corresponding to the first classification identifier.
 17. Themethod of claim 16, comprising: retrieving a first Neugebauer PrimaryArea Coverage (NPAC) from the first LUT to use with the color mappingprocess of the first set of image data; selecting a second LUT of colorspace values based on a second classification identifier correspondingto a second set of image data; retrieving a second NPAC from the secondLUT to use with the color mapping process of the second set of imagedata corresponding to the second classification identifier; andgenerating a halftone image of the first set of image data and thesecond set of image data using the first NP for image data correspondingto the first classification identifier and using the second NP for imagedata corresponding to the second classification identifier.
 18. Themethod of claim 17, wherein: the first LUT includes NPACs with a numberof Neugebauer Primaries (NPs) per NPAC that is different from a numberof NPs per NPAC of the second LUT; and the first LUT includes rescaledarea coverages corresponding to a difference in area coverage of the NPsin corresponding LUT entries.
 19. The method of claim 16, whereingenerating control data comprises: selecting a halftone process for thefirst set of image data based on the first classification identifier;and generating an output halftone image by performing the selectedhalftone process on the first set of image data using the first LUTcorresponding to the classification identifier.
 20. The method of claim16, comprising: selecting a LUT for each classification identifierassociated with input image data, the input image data comprising thefirst set of image data corresponding to the first classificationidentifier and a second set of image data corresponding to a secondclassification identifier; and performing the color mapping process foreach pixel of the input image data using a resource set corresponding tothe classification identifier associated with the respective pixel, theresource set comprising a LUT and a halftone matrix.
 21. The method ofclaim 16, wherein the first classification identifier corresponds to aregion of the image data.